Semi-supervised generic descriptor in face recognition
Pang Ying Han, Ooi Shih Yin, Goh Fan Ling · 2015
Supervised learning techniques are preferable in face recognition for their pleasant data discriminating capability. However, their performance just can be assured if and only if there are sufficient labelled training images available. Practically, it always happens that only a small number of labelled training images available due to costly and time consuming labelling process. On the other hand, a large pool of unlabeled data could be easily obtained through public databases like Google or Flickr. Hence, semi-supervised learning is an alternative direction in face recognition. Semi-supervised techniques utilize limited labelled training images and huge amount of unlabeled training data for data learning. This paper presents a new semi-supervised technique, namely Semi-supervised Generic Descriptor (SSGD). SSGD uses labelled training images to compute the null space of class scatter vector and generate class generic descriptors to represent each class. Besides that, unlabelled training images are exploited to obtain more information about face data structure. The empirical results demonstrate that SSGD shows relatively promising performance in face verification.